Multi-Layer Adversarial Prompt Detection System: Protecting Large Language Models from Malicious Input Attacks

This article introduces an innovative multi-layer protection architecture that achieves real-time detection and defense against prompt injection and jailbreak attacks on large language models through a three-layer gated pipeline consisting of rule filtering, machine learning classification, and semantic analysis, aiming to address the core threats to LLM security.

While large language models (LLMs) are widely used, prompt injection attacks (overriding system instructions to induce unintended operations) and jailbreak attacks (bypassing security restrictions to generate harmful content) have become major threats. Traditional single protection methods have limitations: rule-based methods are easily bypassed by new attacks, pure machine learning solutions perform poorly against zero-day attacks, and deep learning semantic analysis has high computational overhead, so an integrated solution is urgently needed.

The system adopts a three-layer gated pipeline architecture:

1. Rule Filtering Layer: Predefined regular expressions and keyword matching to identify known attack patterns in milliseconds, quickly allowing normal requests to pass and reducing the burden on subsequent layers;
2. Machine Learning Classification Layer: Based on TF-IDF feature extraction and LightGBM gradient boosting trees, it learns statistical features of attacks to identify variant attacks that the rule layer cannot capture, with interpretability;
3. Semantic Analysis Layer: Uses Sentence-BERT to encode sentence vectors, captures deep semantics, and detects camouflaged complex attacks (such as indirect instructions via metaphors or role-playing).

1. Gated Design: Only suspicious inputs enter the next layer, reducing average processing latency;
2. Dynamic Updates: Supports real-time updates of the rule base and regular retraining of models to adapt to evolving threats;
3. Logging and Alerts: Records detection decisions (confidence levels and results of each layer) to facilitate audit tracing and model improvement.

It can be applied to customer service robots (preventing sensitive information leakage), content generation platforms (blocking non-compliant content), and enterprise-level AI applications (internal system protection). The modular design is easy to integrate into existing LLM service architectures and supports independent API or microservice deployment.

This system integrates the advantages of rules, machine learning, and deep learning, balancing detection speed, accuracy, and generalization ability. Future expansion directions: introducing reinforcement learning to achieve adaptive protection, combining federated learning to share threat intelligence, and continuously promoting innovation in LLM security protection technology.